Automatic SCF acquisition systems typically consist broadly of two
major components: hypothesis generation and hypothesis selection. As a
pre-processing step, a corpus of text is processed with a natural
language parser to produce a syntactic analysis for each sentence. The
hypothesis generator uses the parser output to decide which SCF is
taken by each verb in each sentence. These hypotheses are then
amalgamated into a lexicon, which consists of a set of hypothesized SCFs for each
verb appearing in the corpus.

The larger the corpus, the more likely
it is that the lexicon will capture a comprehensive set of SCFs for
each verb. However, the output of the hypothesis generation step
is typically noisy, due to the difficulty of the task: ideally the SCF acquisition system does not make use of lexical information such as SCF dictionaries, since this is 
% exactly 
what is being acquired, although such dictionaries are routinely used in other NLP tasks.
% owing to parser errors as well as errors of the
% hypothesis generator itself, 
Thus a filtering step is required to select from among the hypotheses those that are most reliable. Filtering is a challenging task,
% however, 
since some SCFs are inherently rare; 
infrequent attestation does not mean an SCF should be filtered out of the lexicon.
% and thus should not be omitted from the lexicon simply because they are infrequently attested.

Within these broad outlines, approaches vary along several dimensions; 
see \cite{schulteimwalde:09} for an overview. Hypothesis generation
may involve a shallow parser (chunker) or a deep grammatical
parser. The SCF inventory may be manually defined, in which case the
task of hypothesis generation involves matching the syntactic analyses
to the pre-defined SCFs; or the SCF inventory may be learned directly from the corpus.
% , in which case a clustering step is typically used. 
The size of SCF
inventories can vary widely between systems, from only a few
% \cite{brent:91,brent:93}
to
some two hundred 
% a couple of hundred 
SCFs, although more recent state of the art systems for general language tend to use
relatively large inventories. 
% The granularity
% of the inventory is closely tied to the amount of semantic information
% incorporated in the frames; for example, {\it raising} and {\it
%   control} are linguistic constructions which are syntactically
% similar but semantically distinct. 
There are a number of mechanisms
for generating hypotheses, as well, using a variety of cues in the
parsed text to identify the SCFs.

% It is possible to retain a noisy lexicon and treat every hypothesized
% SCF as an absolute indicator, but typically the cues used in the
% hypothesis generation stage are regarded as probabilistic indicators
% of a particular verb taking an SCF. Many systems thus employ a
% rigorous filtering step after the noisy hypothesis generation
% step. Filtering typically employs statistical hypothesis tests to
% identify likely SCFs. This is a challenging task, however, since some
% SCFs are inherently rare, and thus should not be discarded simply
% because they are infrequently attested.

A number of SCF acquisition systems have been developed for general
language (usually newswire)
text \citep{korhonen:02,valex,preiss:07}. Very good accuracy has been
obtained, although the best results use sophisticated methods such as
smoothing the SCF distributions smoothing based on the semantic
classes of the verbs \cite{korhonen:02}.  Here, we wish to see how
state of the art methods translate to biomedicine.  We evaluated two
automatically acquired lexicons:
% systems: 
the BioLexicon \cite{biolexicon} and 
a new lexicon which we call BioVALEX.
% the Cambridge system \cite{briscoe:97,preiss:07}. 
The Biolexicon was built using tools adapted to a subdomain of biomedicine. 
BioVALEX was built using tools developed for general language \cite{briscoe:97,preiss:07}, and we have adapted them 
% The Cambridge system was built for general language and we have adapted it 
only by applying 
them
% the system 
to a biomedical rather than a general domain corpus.
% , in order to obtain a view of how well such a system works in biomedicine.
